Numerical Prediction of Turbulent Drag Reduction with Different Solid Fractions and Distribution Shapes over Superhydrophobic Surfaces

Hoai Thanh Nguyen, Kyoungsik Chang (), Sang-Wook Lee, Jaiyoung Ryu and Minjae Kim
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Hoai Thanh Nguyen: School of Mechanical Engineering, University of Ulsan, Ulsan 44610, Korea
Kyoungsik Chang: School of Mechanical Engineering, University of Ulsan, Ulsan 44610, Korea
Sang-Wook Lee: School of Mechanical Engineering, University of Ulsan, Ulsan 44610, Korea
Jaiyoung Ryu: Mechanical Engineering, Chung-Ang University, Seoul 06974, Korea
Minjae Kim: Agency for Defense Development, Changwon 51678, Korea

Energies, 2022, vol. 15, issue 18, 1-21

Abstract: The exploration of superhydrophobic drag reduction has been and continues to be of significant interest to various industries. In the present work, direct numerical simulation (DNS) is utilized to investigate the effect of the parameters on the drag-reducing performance of superhydrophobic surfaces (SHS). Simulations with a friction Reynolds number of 180 were carried out at solid fraction values of ϕ s = 1 16 , 1 11 , and 1 4 , and three distribution shapes: aligned, staggered, and random. The top wall is the smooth one, and the bottom wall is a superhydrophobic surface (SHS). Drag reduction and Reynolds stress profiles are compared for all cases. The turbulent kinetic energy budget, including production, dissipation, and diffusion, is presented with respect to the solid fraction and type of distribution to investigate the drag reduction mechanism. The sizes of the longitudinal vortices and formation of hairpin vortices are investigated through the observation of coherent structures. The simulation of a post model is a useful method to study the drag reduction for different solid fraction values and distribution geometries. Our study demonstrates that the drag reduction could acquire 42% with the solid fraction value ϕ s = 1 16 and an aligned distribution shape for post superhydrophobic surface geometry. Our study also showed the relationship of the Reynolds stress component (R 11 , R 22 , and R 33 ) to the drag reduction with the differences in the solid fraction values and distribution geometry. In which, the R 11 component has the most change between an aligned distribution and a random one. The peak value of R 11 tends to shift away from the SHS wall. In addition, the analysis of the TKE budget over the superhydrophobic surface was performed, which can be adopted as a useful resource in turbulence modeling based on RANS methodology.

Keywords: DNS; superhydrophobic surface; drag reduction; turbulent flow (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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